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Activity Number: 84
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321282
Title: A Dynamic Spatio-Temporal Model for Areal Data Applied to Dengue Disease Mapping
Author(s): Gavino Puggioni*
Companies: University of Rhode Island
Keywords: Gaussian Markov Random Field ; Disease Risk ; spatio-temporal model ; Areal Data ; Dengue ; CAR models

One of the most common applications of areal data models is the study of disease risk mapping. While several existing methods are very flexible in describing different forms of spatial heterogeneity and correlation, they often do not capture some of the dynamic features, especially when a dataset spans over a long time range. Motivated by the analysis of dengue virus infections data in Puerto Rico, a new model is formulated, with the inclusion of DLM components in addition to a standard CAR prior structure for the spatial random effect. The specification can account for trends, seasonality, and the temporal change in the effect of land coverage, meteorological, and environmental covariates. Estimation is carried using MCMC methods.

Authors who are presenting talks have a * after their name.

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